Švecová Monika, Blahová Linda, Kostolný Jozef, Birková Anna, Urdzík Peter, Mareková Mária, Dubayová Katarína
Department of Medical and Clinical Biochemistry, Faculty of Medicine, Pavol Jozef Šafárik University in Košice, Tr. SNP 1, 040 01, Košice, Slovakia.
Department of Informatics, Faculty of Management Sciences and Informatics, University of Žilina, Univerzitná 8215/1, 010 26, Žilina, Slovakia.
Talanta. 2025 Feb 1;283:127083. doi: 10.1016/j.talanta.2024.127083. Epub 2024 Oct 21.
Endometrial cancer (EC) is the most prevalent cancer within the female reproductive system in developed countries. Despite its high incidence, there is currently no established laboratory screening test for EC, making early detection challenging. This study introduces an innovative, minimally invasive, and cost-effective method utilizing three-dimensional fluorescence analysis combined with machine learning algorithms to enhance early EC detection. Intrinsic fluorescence of blood serum samples was measured using a luminescence spectrophotometer, which captured fluorescence spectra as synchronous excitation spectra and visualized them through wavelength contour matrices. The spectral data were processed using machine learning algorithms, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD), along with exploratory techniques such as Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). Fluorescence ratios R300/330 and R360/490, indicative of altered tryptophan metabolism and redox state changes, were identified as fluorescent spectral markers and represent key metabolic biomarkers. These ratios demonstrated high diagnostic efficacy with AUC values of 0.88 and 0.91, respectively. Among the ML algorithms, LR and RF exhibited high sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), showing significant promise for clinical application. After optimization, LR achieved a sensitivity of 0.94, specificity of 0.89, and an impressive AUC value of 0.94. The application of this novel approach in laboratory diagnostics has the potential to significantly enhance early detection and improve prognosis for EC patients.
子宫内膜癌（EC）是发达国家女性生殖系统中最常见的癌症。尽管其发病率很高，但目前尚无针对EC的既定实验室筛查测试，这使得早期检测具有挑战性。本研究引入了一种创新的、微创且具有成本效益的方法，该方法利用三维荧光分析结合机器学习算法来加强EC的早期检测。使用发光分光光度计测量血清样本的固有荧光，该分光光度计将荧光光谱作为同步激发光谱进行采集，并通过波长轮廓矩阵将其可视化。光谱数据使用机器学习算法进行处理，包括随机森林（RF）、逻辑回归（LR）、支持向量机（SVM）和随机梯度下降（SGD），以及主成分分析（PCA）和偏最小二乘判别分析（PLS - DA）等探索性技术。荧光比率R300/330和R360/490，分别指示色氨酸代谢改变和氧化还原状态变化，被确定为荧光光谱标志物，并代表关键的代谢生物标志物。这些比率分别具有0.88和0.91的AUC值，显示出高诊断效能。在机器学习算法中，LR和RF表现出高灵敏度、特异性、阳性预测值（PPV）和阴性预测值（NPV），显示出显著的临床应用前景。经过优化后，LR实现了0.94的灵敏度、0.89的特异性和令人印象深刻的0.94的AUC值。这种新方法在实验室诊断中的应用有可能显著提高EC患者的早期检测率并改善预后。
